Enterprise Search

Poor data quality gives search a bad rap

If you’re involved in managing the enterprise search instance at your company, there’s a good chance that you’ve experienced at least some users complain about the poor results they see. 

The common lament search teams hear is “Why didn’t we use Google?” when in fact, sites that implemented the GSA but don’t utilize the Google logo and look, we’ve seen the same complaints.

We're often asked to come in and recommend a solution. Sometimes the problem is simply using the wrong search platform: not every platform handles every user case and requirement equally well. Occasionally, the problem is a poorly or misconfigured search, or simply an instance that hasn’t been managed properly. Even the renowned Google public search engine doesn’t happen by itself, but even that is a poor example: in recent years, the Google search has become less of a search platform and more of a big data analytics engine.

Over the years, we’ve been helping clients select, implement, and manage Intranet search. In my opinion, the problem with search is elsewhere: Poor data quality. 

Enterprise data isn’t created with search in mind. There is little incentive for content authors to attach quality metadata in the properties fields of Adobe PDF Maker, Microsoft Office, and other document publishing tools. To make matters worse, there may be several versions of a given document as it goes through creation, editing, reviews, and updates. And often the early drafts, as well as the final version, are in the same directory or file share. Very rarely does a public facing web site content have such issues.

Sometimes content management systems make it easy to implement what is really ‘search engine optimization’ or SEO; but it seems all too often that the optimization is left to the enterprise search platform to work out.

We have an updated two-part series on data quality and search, starting here. We hope you find it helpful; let us know if you have any questions!

First Impressions on the new Forrester Wave

The new Forrester Wave™: Cognitive Search And Knowledge Discovery Solutions is out, and once again I think Forrester, along with Gartner and others, miss the mark on the real enterprise search market. 

In the belief that sharing my quick first impression will at least start a conversation going until I can write up a more complete analysis, I am going to share these first thoughts.

First, I am not wild about the new buzzterms 'cognitive search' and "insight engines". Yes, enterprise search can be intelligent, but it's not cognitive. which Webster defines as "of, relating to, or involving conscious mental activities (such as thinking, understanding, learning, and remembering)". HAL 9000 was cognitive software; "Did you mean" and "You might also like" are not cognition.  And enterprise search has always provided insights into content, so why the new 'insight engines'? 

Moving on, I agree with Forrester that Attivio, Coveo and Sinequa are among the leaders. Honestly, I wish Coveo was fully multi-platform, but they do have an outstanding cloud offering that in my mind addresses much of the issue.

However, unlike Forrester, I believe Lucidworks Fusion belongs right up there with the leaders. Fusion starts with a strong open source Solr-based core; an integrated administrative UI; a great search UI builder (with the recent acquisition of Twigkit); and multiple-platform support. (Yep, I worked there a few years ago, but well before the current product was created).

I count IDOL in with the 'Old Guard' along with Endeca, Vivisimo (‘Watson’) and perhaps others - former leaders still available, but offered by non-search companies, or removed from traditional enterprise search (Watson). And it will be interesting to see if Idol and its new parent, Microfocus, survive the recent shotgun wedding. 

Tier 2, great search but not quite “full” enterprise search, includes Elastic (which I believe is in the enviable position as *the* platform for IoT), Mark Logic, and perhaps one or two more.

And there are several newer or perhaps less-well known search offerings like Algolia, Funnelback, Swiftype, Yippy and more. Don’t hold their size and/or youth against them; they’re quite good products.

No, I’d say the Forrester report is limited, and honestly a bit out of touch with the real enterprise search market. I know, I know; How do I really feel? Stay tuned, I've got more to say coming soon. What do you think? Leave a comment below!

The Findwise Enterprise Search and Findability Survey 2016 is open for business

Would you find it helpful to benchmark your Enterprise Search operations against hundreds of corporations, organizations and government agencies worldwide? Before you answer, would you find that information useful enough that you’re spend a few minutes answering a survey about your enterprise search practices? It seems like a pretty good deal to me to have real-world data from people just like yourself worldwide.

This survey, the results of which are useful, insightful, and actionable for search managers everywhere, provides the insight into many of the critical areas of search.

Findwise, the Swedish company with offices there and in Denmark, Norway Poland, Norway and London, is gathering data now for the 2016 version of their annual Enterprise Search and Findability Survey at http://bit.ly/1sY9qiE.

What sorts of things will you learn?

Past surveys give insight into the difference between companies will happy search users versus those whose employees prefer to avoid using internal search. One particularly interesting finding last year was that there are three levels of ‘search maturity’, identifiable by how search is implemented across content.

The least mature search organizations, roughly 25% of respondents, have search for specific repositories (siloes), but they generally treat search as ‘fire and forget’, and once installed, there is no ongoing oversight.

More mature search organizations that represent about 60% of respondents, have one search for all silos; but maintaining and improving search technology has very little staff attention.

The remaining 15% of organizations answering the survey invest in search technology and staff, and continuously attempt to improve search and findability. These organizations often have multiple search instances tailored for specific users and repositories.

One of my favorite findings a few years back was that a majority of enterprises have “one or less” full time staff responsible for search; and yet a similar majority of employees reported that search just didn’t work. The good news? Subsequent surveys have shown that staffing search with as few as 2 FTEs improves overall search satisfactions; and 3 FTEs seem to strongly improve overall satisfaction. And even more good news: Over the years, the trend in enterprise search shows that more and more organizations are taking search and findability seriously.

You can participate in the 2016 Findwise Enterprise Search and Findability Survey in just 10 or 15 minutes and you’ll be among the first to know what this year brings. Again, you’ll find the 2016 survey at http://bit.ly/1sY9qiE.

Your enterprise search is like your teenager

During a seminar a while back, I made this spontaneous claim. Recently, I made the comment again, and decided to back up my claim - which I’ll do here.

No, really – it’s true. Consider:

You can give your search platform detailed instructions, but it may or may not do things the way you meant:

Modern search platforms provide a console where you, as the one responsible for search, can enter all of the information needed to index content and serve up results. You tell it what repositories to index; what security applies to the various repositories; and how you want the results to look.  But did it? Does it give you a full report of what it did, what it was unable to do, and why?

You really have no idea what it’s doing – especially on weekends:

 Search platforms are notorious for the lack of operational information they provide.

Does your platform give you a useful report of what content was indexed successfully, and which were not – and why? And some platforms stop indexing files when they reach a certain size: do you know what content was not completely indexed?

When it does tell you, sometimes the information is incomplete: 

Your crawler tells you there were a bunch of ‘404’ errors because of a bad or missing URL; but will it tell you which page(s) had the bad link? Chances are it does not. 

They can be moody, and malfunction without any notice:

You schedule a full update of you index every weekend, and it has always worked flawlessly – as far as you know. Then, usually on a 3-day weekend, it fails. Why? See above.

When you talk to others who have search, theirs always sounds much better than yours:

As a conscientious search manager, you read about search, you attend webinars and conferences, and you always want to learn more. But you wonder why other search mangers seem to describe their platform in glowing terms, and never seem to have any of the behavioral issues you live with every day. It kind of makes you wonder what you’re doing wrong with yours.

It costs more to maintain than you thought and it always needs updates:

When you first got the platform you knew there we ongoing expenses you’d have to budget – support, training, updates, consulting. But just like your kid who needs books, a computer, soccer coaching, and tuition, it’s always more than you budgeted. Sometimes way more!

You can buy insurance, but it never seems to cover what you really need:

Bear with me here: you get insurance for your kids in case they get sick or cause an accident, and you buy support and maintenance for your search platform.  But in the same way that you end up surprised that orthodontics are not fully covered, you may find out that help tuning the search platform, or making it work better, isn’t covered by the plan you purchased – in fact, it wasn’t even offered. QED.

It speaks a different vocabulary:

You want to talk with your kid and understand what’s going on; you certainly don’t want to look uncool. But like your kid, your search platform has a vocabulary that only barely makes sense to you. You know rows and columns, and thought you understood ‘fields’; but the search platform uses words you know but that don’t seem to be the same definition you’ve known from databases or CMS systems.

It's hard for one person to manage, especially when it's new:

Many surveys show that most companies have one (or less) full-time staff responsible for running the search engine – while the same companies claim search is ‘critical’ to their mission.  Search is hard to run, especially in the first few years when everything needs attention. You can always get outside help – not unlike day care and babysitters – but it just seems so much better if you could have a team to help manage and maintain search to make it behave better.

How it behaves reflects on you:

You’re the search manager and you’ve got the job to make search work “just like Google”.  You spent more than $250K to get this search engine, and the fact that it just doesn’t work well reflects badly on you and your career. You may be worried about a divorce.

It doesn’t behave like the last one:

People tend to be nostalgic, as are many search managers I know. They learned how to take care of the previous one, but this new one – well, it’s NOTHING like the earlier one. You need to learn its habits and behaviors, and often adjust your behavior to insure peace at work.

You know if it messes up badly late at night, even on a weekend or a holiday, you’ll hear about it:

If customers or employees around the world use your search platform, there is no ‘down time’: when it’s having an issue, you’ll hear about it, and will be expected to solve the issue – NOW. You may even have IT staff monitoring the platform; but when it breaks in some odd and unanticipated way, you get the call. (And when does search ever fail in an expected way?)

 You may be legally responsible if it messes up:

Depending on what your search application is used for, you may find yourself legally responsible for a problem. Fortunately, the chances of you personally being at fault are slim, but if your company takes a hit for a problem that you hadn’t anticipated, you may have some ‘career risk’ of your own. Was secure content about the upcoming merger accidentally made public? Was content to be served only to your Swiss employees when they search from Switzerland exposed outside of the country? And you can’t even buy liability insurance for that kind of error.

When it’s good, you rarely hear about it; when it's bad, you’ll hear about it:

Seriously, how many of you have gotten a call from your CIO to tell you what a great experience he or she had on the new search platform? Do people want to take you to lunch because search works so well? If you answered ‘yes’ to either of these, I’d like to hear from you!

In my experience, people only go out of their way to give feedback on search when it’s not working well. It’s not “like Google”. Even though Google has hundreds or people and ‘bots’ examining every search query to try to make the result better, and you have only yourself and an IT guy.

You’ll hear. 

The work of managing it is never done:

The wonderful southern writer Ferrol Sams wrote :

“He's a good boy… I just can't think of enough things to tell him not to do.” Sound like your search platform? It will misbehave (or fail outright) in ways you never considered, and your search vendor will tell you “We’ve never seen a problem like that before”. Who has to get it fixed? You have to ask?

Once it moves away, you sometimes feel nostalgic:

Either you toss it out, or a major upgrade from your vendor comes alone and the old search platform gets replaced. Soon, you’re wishing for the “Good old days” when you knew how cute and quirky the old one was, and you find yourself feeling nostalgic for it and wishing that it didn’t have to move out.

Do you agree with my premise? What  have I missed?

More on the Gartner MQ: Fact or fiction?

There is a lively discussion going on over in the LinkedIn ‘Enterprise Search Engine Professionals’ group about the recent Gartner Magic Quadrant report on Enterprise Search. Whit Andrews, a Gartner Research VP, has replied that the Gartner MQ is not a 'pay to play'. I confess guilt to have been the one who brought the topic up in these threads, at least, and I certainly thank Whit for clarifying the misunderstanding directly.

That said, two of my colleagues who are true search experts have raised some questions I thought should be addressed.

Charlie Hull of UK-based Flax says he's “unconvinced of the value of the MQ to anyone wanting a comprehensive … view of the options available in the search market'. And Otis Gospodnetić of New York-based Sematext asks "why (would) anyone bother with Gartner's reports. We all know they don't necessarily match the reality". I want to try to address those two very good points.

First, I'm not sure Gartner claims to be a comprehensive overview of the search market. Perhaps there are more thorough lists- my friends and colleagues Avi Rappoport and Steve Arnold both have more complete coverage. Avi, now at Search Technologies, still maintains   

www.searchtools.com with a list that is as much a history of search as a list of vendors. And Steve Arnold has a great deal of free content on his site as well as high quality technology overviews by subscription. Find links to both at arnoldit.com.

Nonetheless, Gartner does have published criterion, and being a paid subscriber is not one of them. His fellow Gartner analyst French Caldwell calls that out on his blog. By the way, I have first-hand experience that Gartner is willing to cut some slack to companies that don't quite meet all of their guidelines for inclusion, and I think that adds credence to the claim that everything.

A more interesting question is one that Otis raises: “why would anyone bother with Gartner's reports”?

To answer that, let me paraphrase a well-known quote from the early days of computers: "No one ever got fired for following Gartner's advice". They are well known for having good if not perfect advice - and I'd suspect that in the fine print, Gartner even acknowledges the fallibility of their recommendations. And all of us know that in real life, you can't select software as complex as an enterprise search platform without a proof of concept in your environment and on your content.

The industry is full of examples where the *best* technology loses pretty consistently to 'pretty good' stuff backed by a major firm/analyst/expert. Otis, I know you're an expert, and I'd take what you say as gospel. A VP at a big corporation who is not familiar with search (or his company's detailed search requirements) may not do so. And any one on that VP's staff who picks a platform based solely on what someone like you or I say probably faces some amount of career risk. That said, I think I speak for Otis and Charlie and others when I say I am glad that a number of folks have listened to our advice and are still fully employed!]

So - in summary, I think we're all right. Whit Andrews and Gartner provide advice that large organizations trust because of the overall methodology of their evaluation. Everyone does know it's not infallible, so a smart company will use the 'trust but verify' approach. And they continue to trust you and I, but more so when Gartner or Forrester or one of the large national consulting companies conforms our recommendation. And of not, we have to provide a compelling reason why something else is better for them. And the longer we're successful with out clients, the more credible we become.

 

 

The unspoken "search user contract"

Search usability is a major difference between search that works and search that sucks. 

I recently had lunch with my longtime friend and associate Avi Rappoport from Search Tools Consulting. We had a great time exchanging stories about some of the search problems our clients have. She mentioned one customer who she was sharing best practices when laying out a result list. That brought to mind what I've called the 'search user contract', which users tacitly expect when they use your search on any site, internal or external.

If you are responsible for an instance of search running inside a firewall, even if it's outward facing, you have a problem your predecessors of 15 to 20 years ago* didn't have. Back then, most users didn't have experience with search except the one you provided - so they didn't have expectations of what it could be like.

Fast forward to the present. In addition to your intranet search, virtually everyone in your organization knows, uses, and often loves Amazon, Facebook, Google, Apple, eBay, and others. They know what really great search looks like. They expect you to suggest searches (or even products) on the fly! Search today knows misspelled words and what other products you might like. And as we start to see more machine learning in the enterprise space, it will get even harder.

But most importantly, almost all of the above sites follow the same unspoken user contract:

• On the result list, the search box goes at the top, either across a wide swath of the browser window or in a smaller box on the left-hand side, near the top.
• There is no more than one search box on the results page.
• Search results, numbered or not, show a page title or product name and description and a meaningful description of the product or summary of the document. Sometimes the summary is just a snippet.
• Words that cause the document to be returned are sometimes bolded in the summary.
• Suggestions for the words and phrases you type show up just below the search box (or up in the URL field)
• Facets, when available, go along the left-hand side and/or across the top, just under the search box. Occasionally they can be on the right of the result list.
• Whether facets are displayed on the left or right of the screen, the numbers next to each facet indicate how many results will display when that facet is clicked.
• Best bets and boosted or promoted results show up at the top of the result list and are generally recognizable as recommended or featured results.
• Advertisements or special announcements appear on the right side of the result list.
• Links to the 'next’ or ‘previous' results page appear at the bottom or less often at the top of the result list.
• Generally, when there is very long result list, there may be a limited number of results per page with a 'Next" and "Previous" links. 

Now it's time to look your web sites - public facing as well as behind your firewall. Things we often see on internal or corporate sites include:

• Spelling suggestions in small, dark font very close to the site background color, at the left edge of the content, just above facets. Users don't expect to look there for suggestions, and even if they do look, make the color stand out so users see it**. Don't make the user think!
• An extra search form on the page; one at the top as 'part of our standard header block'; and one right above the result list to enable drill down. The results you see will differ depending on which field to type in. [The visitor is confused: which search button should be pressed to do a 'drill down' search. Again, don't make the user think]
• Tabs for drilling into different content areas seem to be facets, but some of the tabs ('News") have no results. [Facets should only display if, by clicking on a facet, the user can see more content]
• As I said at the top, we’ve found poor search user experience is a major reason employees and site visitors report that ‘search sucks’. One of the standard engagements we do is a Search Audit, which includes search usability in addition to a review of user requirement and expectations.  

 

/s/Miles

 

*Yes, Virginia, there was enterprise search 20 or more years ago. Virtually none of those names still exist, but their technology is still touching you every day. Fulcrum, Verity, Excalibur and others were solving problems for corporations and government agencies; and of course Yahoo was founded in 1994.

**True story, with names omitted to protect the innocent. On a site where I was asked to deliver a search quality audit, ‘spelling suggestions’ was a top requested feature. They actually had spell suggestions, in grey letters in a dark black field with a dark green background, far to the left of the browser window. No one noticed them. You know you are; you’re welcome!

 

What does it take to qualify as 'Big Data'?

If you've been on a deserted island for a couple of decades, you may not have heard the hot new buzz phrase: Big Data. And you many not have heard of "Hadoop", the application that accidentally solved the problem of Big Data.

Hadoop was originally designed as a way for the open source Nutch crawler to store its content prior to indexing. Nutch was fine for crawling sites; but of you wanted to crawl really massive data sets – say the Internet – you needed a better way to store the content (thank goodness Doug Cutting didn’t work at a database giant or we’d all be speaking SQL now!) GigaOm has a great series on the history of Hadoop http://bit.ly/1jOMHiQ I recommend for anyone interested in how it all began and evolved,

After a number of false starts, brick walls, and subsequent successes, Hadoop is a technology that really enables what we now call ‘big data’- usually written as "Big Data". But what does this mean?  After all, there are companies with a lot of data – and there are companies with limited content size that changes rapidly every day. But which of these really have data that meets the 'Big" definition.  

Consider a company like AT&T or Xerox PARC, which licenses its technology to companies worldwide. As part of a license agreement, PARC agrees to defend its licensees if an intellectual property lawsuit ever crosses the transom. Both companies own over tens of thousands patents going back to its founding in the early 20^th century. Just the digital content to support these patents and inventions must number on the tens of millions of documents, much of which is in formats no longer supported by any modern search platform. Heck, to Xerox, WordStar and Peachtext probably seem pretty recent! But about the only time they have to access their content search is when a licensee needs help defending a licensee against an IP claim. I don’t know how often that is, but I’d bet less than a dozen times a year.

Now consider a retail giant like Amazon or Best Buy. In raw size, I’d bet Amazon has hundreds of millions of items to index: books, products, videos, tunes. Maybe more. But that’s not what makes Amazon successful. I think it’s the ability to execute billions of queries every day – again, maybe more – and return damn good results in well under a second, along with recommendations for related products. Best buy actually has retail stores, so they have to keep purchase data, but also buying patterns so they know what products to stock in any given retail location.

A healthcare company like UnitedHealth must have its share of corporate intranet content. But unlike many corporations, these companies must process millions of medical transactions every week: doctor visits, prescriptions, test results, and more. They need to process these transactions, but they also must keep these transactions around for legally defined durations.

Finally, consider a global telecom company like Ericsson or Verizon. They’ve got the usual corporate intranet, I’m sure. They have financial transactions like Amazon and UHG. But they also have telecomm transaction records that must count in the billions a month: phone calls and more. And given the politics of the world, many of these transactions have to be maintained and searchable for months, if not years.

These four companies have a number of common traits with respect to search; but each has its own specific demands. Which ones count as ‘big data’ as it’s usually defined? And which just have ‘a bunch of content?

As it turns out that’s a touch question. At one point, there was a consensus that ‘big data’ required three things, known as the “Three V’s of Big Data’. This escalated to the ‘5 V’s of Big Data’, then the “7 V’s”– and I’ve even seen some define the “10 V’s of Big Data”. Wow.. and growing!

Let’s take a look at the various “V’s” that are commonly used to define ‘Big Data’.

Depending on who you ask, there are four, five, seven or more ‘requirements’ that define ‘big data. These are usually referred to as the “Vs of Big Data”, and these usually include:

Volume: The scale of your data – basically, how many ‘entries’ or ‘items’, you have. For Xerox, how many patents; for a telecom company, how many phone ‘transactions’ have there been.   

Variety: Basically this means how many different types of data you have. Amazon has mouse clicks, product views, unique titles, subscribers, financial transactions and more. For UHG and Ericsson, I’d guess the majority of their content is transactional: phone call metadata (originating and receiving phone number, duration of the call, time of day, etc.). In the enterprise, variety can also mean data format and structure. Some claim that 90% of enterprise data is unstructured, which adds yet another challenge.

Veracity: The boils down whether the data is trustworthy and meaningful. I remember a survey HP did years ago to find out what predictors were useful to know whether a person waking into a random electronics store would walk out with an HP PC. Using HP products at work or at home we the big predictors; but the fact that the most likely day was Tuesday was perhaps spurious and not very valuable.

Velocity: How fast is the data coming in and/or changing. Amazon has a pretty good idea on any given day how many transactions they can expect, and Verizon knows how much call data they can expect. But things change: A new product becomes available, or a major world event triggers many more phone calls than usual.

Viability: If you want to track trends, you need to know what data points are the most useful in predicting the future. A good friend of mine bought a router on Amazon; and Amazon reported that people who bought that router also bought.. men’s extra large jeans. Now, he tells me he did think they were nice jeans, but that signal may not have had long viability.

Value: How useful or important is the data in making a prediction, or in improving business decisions. That was easy!

Variability: This often refers to how internally consistent the data is. To a data point as an accurate predictor, that data point is ideally consistent across the wide range of content. Blood pressure, for example, is generally in a small range; and for a given patient, should be relatively consistent over time. When there is a change, UHG may want to understand the cause.

Visualization: Rows and columns of data can look pretty intimidating and it’s not easy to extract meaning from them. But as they say, ‘a picture is worth a thousand words’, so being able to see charts or graphs can help meaning and trends jump out at you.  I’d use Lucidworks’ SiLK product as an example of a great visualization tool for big data, but there are many others.

Validity: This seems like another way to say the data has veracity, but it may be a subtle point. If you’re recording click-thru data, or prescriptions, or intellectual property, you have to know that the data is accurate and internally consistent. In my HP anecdote above, is the fact that more people bought HP PCs on Tuesday a valid finding? Or is it simply noise? You’ll probably need a human researcher to make these kinds of calls.

Venue: With respect to Big Data, this means where the data came from and where it will be used. Content collected from automobiles and from airplanes may look similar in a lot of ways to the novice. In the same way, data from the public Internet versus data collected from a private cloud may look almost identical. But making decisions for your intranet based on data collected from Bing or Google may prove to be a risk.

Vocabulary: What describes or defines the various items of the data. Ericsson has to know which bit of data represent a phone number and which represent the time of day. Without some idea of the schema or taxonomy, we’ll be hard pressed to reach reasonable decisions from Big Data.

Volatility: This may seem like velocity above, but volatility in Big Data really means how long is the data value, how long do you need to keep it around.  Healthcare companies may need to keep the data a lot longer than

Vagueness: This final one is credited to Venkat Krishnamurthy of YarcData just last month at the Big Data Innovation Summit here in Silicon Valley.  In a way, it addresses the confidence we can have in the results suggested by the data. Are we seeing real trends, or are we witnessing a black swan?

In the application of Big Data not all of these various V’s are as valid or valuable to the casual (or serious) observer. But as in so many things, interpreting the data is to the person making the call. Big Data is only a tool: use it wisely!

Some resources I used in collection data for this article include the follow web sites and blogs:

IBM’s Big Data & Analytics Hub 

MapR's Blog: Top 10 Big Data Challenges – A Serious Look at 10 Big Data V’s 

See also Dr. Kirk Borne’s Top 10 List on Data Science Central   

Bernard Marr’s LinkedIn post on The 5 Vs Everyone Must Know 

 

A paradigm shift in enterprise search

I've been involved in enterprise search since before the 'earthquake World Series' between the Giants and the A's in 1989. While our former company became part of LucidWorks last December, we still keep abreast of the market. But being a LucidWorks employee has brought me to a new realization: commercial enterprise search is pretty much dead.

Think back a few years: FAST ESP, Autonomy IDOL (including the then-recently acquired Verity), Exalead, and Endeca were the market. Now, every one of those companies has become part of a larger business. Some of the FAST technology lives on, buried in SharePoint 2013; Autonomy has suffered as part of HP because - well, because HP isn't what it was when Bill and Dave ran it. Current management doesn't know what they have in IDOL, and the awful deal they cut was probably based on optimistic sales numbers that may or may not have existed. Exalead, the engine I hoped would take the place of FAST ESP in the search market is now part of Dassault and is rarely heard of in search. And Endeca, the gem of a search platform optimized for the lucrative eCommerce market, has become one of three or four search-related companies in the Oracle stable. 

Microsoft is finally taking advantage of the technology acquired in the FAST acquisition for SharePoint 2013, but as long as it's tied to SharePoint - even with the ability to index external content - it's not going to be an enterprise-wide distribution - or a 'big data' solution. SharePoint Hadoop? Aslongf as you bring SQL Server. Mahout? Pig? I don't think so. There are too many companies that want or need Linux for their servers rather than Windows.

Then there is Google, the ultimate closed-box solution. As long as you use the Google search button/icon, users are happy – at least at first. If you have sixty guys named Sarah? Maybe not.

So what do we have? A few good options generally from small companies that tend to focus on hosted eCommerce - SLI Systems and Dieselpoint; and there’s Coveo, a strong Windows platform offering.

Solr is the enterprise search market now. My employer, LucidWorks, was the first, and remains the primary commercial driver to the open source Apache project. What's interesting is the number of commercial products based on Solr and it's underlying platform, Lucene.

Years ago, commercial search software was the 'safe choice'. Now I think things have changed: open source search is the safe choice for companies where search is mission. Do you agree?

I'll be writing more about why I believe this to be the case over the coming weeks and months: stay tuned.

/s/Miles

 

Last call for submiting papers to ESS NY

This Friday, December 21, is the last day for submitting papers and workshops to ESS in NY in May 21-22. See the information site at the Enterprise Search Summit Call for Speakers page.

If you work with enterprise search technologies (or supporting technologies), chances are the things you've learned would be valuable to other folks. If you have an in-depth topic, write it up as a 3 hour workshop; if you have a success story, or lessons learned you can share, submit a talk for a 30-45 minute session.

I have to say, this conference has enjoyed a multi-year run in terms of quality of talks and excellent Spring weather.. see you in May?

 

 

Kipling on the future of Autonomy

There's a line in Rudyard Kipling's poem 'If" I always found inspiring:

"Or watch the things you gave your life to, broken,
And stoop and build'em up with worn-out tools;"

Born some 150 years ago in South Africa, Englishman Rudyard Kipling seems to have little to offer for Autonomy founder, former HP employee, and newly minted venture capitalist Mike Lynch. On the other hand, I think I may see an investment opportunity he may like.

Do you suppose he would invest in a company that grew to be a leader in its field, but which languished after acquisition by a giant firm seemingly past its prime? Would he seek to acquire such a company, perhaps at a significant discount to the price paid to acquire it just a few years ago - and make that company back into the respected - and feared - leader it once was? Let's say selling a company for $11B in 2011 and buying it back for $3B in 2013? 

I can't say; I'm just a small business owner with little experience in VC. On the other hand, think what a coup it would be for Autonomy to rise from the ashes, and confirm the genius of its founder.

Stay tuned.